Instructions to use BELLE-2/BELLE-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BELLE-2/BELLE-VL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BELLE-2/BELLE-VL", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("BELLE-2/BELLE-VL", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BELLE-2/BELLE-VL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BELLE-2/BELLE-VL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BELLE-2/BELLE-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BELLE-2/BELLE-VL
- SGLang
How to use BELLE-2/BELLE-VL with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "BELLE-2/BELLE-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BELLE-2/BELLE-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "BELLE-2/BELLE-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BELLE-2/BELLE-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BELLE-2/BELLE-VL with Docker Model Runner:
docker model run hf.co/BELLE-2/BELLE-VL
Download config.json from BELLE-2/BELLE-VL: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/BELLE-2/BELLE-VL/resolve/fa5e90de30cf3295ca886fdf4812f44012fae0af/config.json
- Command line
-
hf download hf://BELLE-2/BELLE-VL@fa5e90de30cf3295ca886fdf4812f44012fae0af/config.json
-
curl -L -o config.json https://huggingface.co/BELLE-2/BELLE-VL/resolve/fa5e90de30cf3295ca886fdf4812f44012fae0af/config.json
1.17 kB
| { | |
| "_name_or_path": "Belle-vl", | |
| "architectures": [ | |
| "QWenLMHeadModel" | |
| ], | |
| "attn_dropout_prob": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_qwen.QWenConfig", | |
| "AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel" | |
| }, | |
| "bf16": true, | |
| "emb_dropout_prob": 0.0, | |
| "fp16": false, | |
| "fp32": false, | |
| "hidden_size": 5120, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 27392, | |
| "kv_channels": 128, | |
| "layer_norm_epsilon": 1e-06, | |
| "max_position_embeddings": 8192, | |
| "model_type": "qwen", | |
| "no_bias": true, | |
| "num_attention_heads": 40, | |
| "num_hidden_layers": 40, | |
| "onnx_safe": null, | |
| "rotary_emb_base": 10000, | |
| "rotary_pct": 1.0, | |
| "scale_attn_weights": true, | |
| "seq_length": 2048, | |
| "tie_word_embeddings": false, | |
| "tokenizer_type": "QWenTokenizer", | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.33.0", | |
| "use_cache": false, | |
| "use_dynamic_ntk": true, | |
| "use_flash_attn": false, | |
| "use_logn_attn": true, | |
| "visual": { | |
| "heads": 16, | |
| "image_size": 448, | |
| "image_start_id": 151857, | |
| "layers": 48, | |
| "mlp_ratio": 4.9231, | |
| "output_dim": 5120, | |
| "patch_size": 14, | |
| "width": 1664 | |
| }, | |
| "vocab_size": 152064 | |
| } | |